A trainable model for predicting property prices
The client partnered with Yellow to create a machine learning solution for conducting predictive analysis of real estate prices. The task for the ML real estate model was to predict what the price of a real estate property will be in a month.
We were responsible for
housing units were for sale in 2021
is the average sales price of new homes
is the homeownership rate in the United States
new houses were sold in the USA in 2021
of buyers find their new home online
is the average number of days houses stay on the market
Defining the property value for a real estate property is a complex process in the USA. In addition to using the available online tools to determine the value by yourself, there are also professional opinions that should be taken into account.
The real estate industry is implementing artificial intelligence to enhance its predictive ability and improve performance.
Specialists use artificial intelligence and machine learning in commercial real estate to predict what properties will be for sale in 12 months so that realtors can efficiently hunt for new listings to meet buyers’ demands.
The analysis language used in property descriptions can help in defining real estate prices since the most popular words are different for cheap and expensive properties.
According to McKinsey, nearly 60% of predictive power can be achieved by using nontraditional variables like proximity to luxury hotels or the number of coffee shops within a mile.
Automated property management
“Smart home” systems
AI-augmented customer service
Enhanced matching of sellers and buyers
To conduct predictive analysis of real estate prices, we created a machine learning model. Here is the strategy we used to develop it.
We used the following technologies to develop machine learning for real estate.
Unfortunately, the dataset used for this project was not sufficient for training the model. It also contained several mistakes that could influence the final results the model would provide.
Our specialists put all their effort into complementing the dataset with the necessary data. We used several available sources of information related to the real estate market in the US, as well as data related to the country’s economic conditions, in order to achieve a more representative data set. In this way, we improved the quality of the data for more accurate results.
Due to the initial quality of the dataset, we faced an issue with underfitting, namely that the model couldn’t find the underlying trends in the present data and provide accurate results.
In addition to adding more quantitative elements to the dataset, we also improved its quality by adding more relevant features. This enabled us to overcome the underfitting issue.
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